ArticleMathematical biosciences2021
Mathematical modeling of spatio-temporal population dynamics and application to epidemic spreading.
Article in Mathematical biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- MEmilio: a high performance Modular EpideMIcs simuLatIOn software for multi-scale and comparative simulations of infectious disease dynamics.Scientific reports · 2026Article
- Systematic prediction of spatiotemporal transmission of potential respiratory pandemics in China.Infectious Disease Modelling · 2026Article
- Stochastic modelling of viral infection spread via a Partial Integro-Differential Equation.Infectious Disease Modelling · 2025Article
- Hybrid metapopulation agent-based epidemiological models for efficient insight on the individual scale: A contribution to green computing.Infectious Disease Modelling · 2025Article
- Information and Knowledge Diffusion Dynamics in Complex Networks with Independent Spreaders.Entropy (Basel, Switzerland) · 2025Article
- Multi-Grid Reaction-Diffusion Master Equation: Applications to Morphogen Gradient Modelling.Bulletin of mathematical biology · 2024Article
- Insights into drivers of mobility and cultural dynamics of African hunter-gatherers over the past 120 000 years.Royal Society open science · 2023Article
- Neural parameter calibration for large-scale multiagent models.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Stochastic SIR model predicts the evolution of COVID-19 epidemics from public health and wastewater data in small and medium-sized municipalities: A one year study.Chaos, solitons, and fractals · 2022Article
- Regional opening strategies with commuter testing and containment of new SARS-CoV-2 variants in Germany.BMC infectious diseases · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agent based models (ABMs) are a useful tool for modeling spatio-temporal population dynamics, where many details can be included in the model description. Their computational cost though is very high and for stochastic ABMs a lot of individual simulations are required to sample quantities of interest. Especially, large numbers of agents render the sampling infeasible. Model reduction to a metapopulation model leads to a significant gain in computational efficiency, while preserving important dynamical properties. Based on a precise mathematical description of spatio-temporal ABMs, we present two different metapopulation approaches (stochastic and piecewise deterministic) and discuss the approximation steps between the different models within this framework. Especially, we show how the stochastic metapopulation model results from a Galerkin projection of the underlying ABM onto a finite-dimensional ansatz space. Finally, we utilize our modeling framework to provide a conceptual model for the spreading of COVID-19 that can be scaled to real-world scenarios.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.